A method of detecting the likelihood of a seizure event in a patient includes at successive expirations of a first time interval, determining a first likelihood that the patient is experiencing a seizure based on electrodermal activity and a movement of a limb of the patient. The method also includes at successive expirations of a second time interval, determining whether the patient experienced a seizure in a second time period preceding the determining based on a heart rate of the patient. In response to determining that the third comparison result satisfies at least a third detection criterion, the method compares electrodermal activity and the movement of a limb of the patient to determine a second likelihood. In response to determining that the second likelihood satisfies a second detection criterion, the method triggers presentation of a second alert regarding a potential seizure.

Disclosed are devices, electrodes, systems, methods, and other implementations, including a device that includes an electrode comprising an elongated body, a plurality of electrode contacts disposed on a first side of the elongated body, and a cannulation channel defined along a longitudinal axis of the electrode. The device further includes a guiding mechanism received within the cannulation channel, the guiding mechanism configured to guide the electrode for placement at a target area inside a body of a patient. In some embodiments, the cannulation channel is configured to receive irrigation fluids dispensed through a perforated end located near a leading tip of the elongated body.

The present disclosure provides systems and methods for seizure detection. The method for seizure detection may include receiving a plurality of electroencephalography (EEG) signals over a plurality of channels for a subject, preprocessing the plurality of EEG signals by segmenting the plurality of EEG signals for each channel into a plurality of temporal data segments, extracting a plurality of features from each temporal data segment for each channel, and applying a machine learning algorithm to the plurality of features to perform a seizure binary classification for each temporal data segment for each channel. A control policy may be employed to determine a seizure burden on the aggregated seizure binary classifications. When the seizure burden is equal to or exceeds a threshold, a notification may be generated. The notification may be usable by a healthcare practitioner to assess whether the subject may be at risk of having a seizure.

An object of the present invention is to more appropriately extrapolate a sign of a state that may affect a movement of a user. A state extrapolation device according to the present invention includes a vital sign acquisition unit that acquires a vital sign of a user, and a sign detection unit that uses a learned model that has learned, as training data, sign data about the vital sign related to a predetermined physical condition abnormality, and detects a sign by determining whether or not the vital sign of the user corresponds to a sign of the predetermined physical condition abnormality.

A graphene bio-device for electrotherapy, includes: a flexible substrate; An electrode made of graphene on the flexible substrate; and an insulation layer on the graphene electrode; wherein the graphene bio-device comprises electrodes for ground, reference, recording and stimulation, wherein the graphene bio-device is measured corticography with low noise and alleviated seizure signals successfully by imposing electrical stimulation.

A seizure detection system including one or more circuits, the one or more circuits configured to receive an electroencephalogram (EEG) signal generated based on electrical brain activity of a patient. The one or more circuits are configured to determine metrics based on the EEG signal, the metrics indicating non-linear features of the EEG signal, determine that the EEG signal indicates a candidate seizure by determining, based at least in part on the metrics, a change in the non-linear features of the EEG signal over time, and generate a seizure alert indicating that the EEG signal indicates the candidate seizure. The change in the non-linear features indicates a physiological force that gives rise to the candidate seizure.

A seizure detection system including one or more circuits, the one or more circuits configured to receive an electroencephalogram (EEG) signal generated based on electrical brain activity of a patient. The one or more circuits are configured to determine metrics based on the EEG signal, the metrics indicating non-linear features of the EEG signal, determine that the EEG signal indicates a candidate seizure by determining, based at least in part on the metrics, a change in the non-linear features of the EEG signal over time, and generate a seizure alert indicating that the EEG signal indicates the candidate seizure. The change in the non-linear features indicates a physiological force that gives rise to the candidate seizure.

A brain mapping system and methods for providing personalized therapy for a broad range of brain dysfunctions by determining the location and the extent of the brain regions that have to be therapeutically targeted in each subject. The present invention includes means to record specific characteristics of brain activity, detect and display brain regions that present signatures of disease or dysfunctions by using a computing system. The therapy is tuned to target detected brain regions to restore specific connectivity characteristics using invasive, non-invasive stimulation, neurofeedback or drug administration. While connectivity characteristics are estimated based on resting state recordings the therapy will be performed in successive steps to alter network fragmentation in dysfunctional brain regions. The improved treatment is tailored to individual patients that will learn how to reshape specific connectivity characteristics to target the determined location and the extent of brain regions and maximize the therapeutic potential. The brain mapping technology is suited for different technologies and not limited to electroencephalography (EEG) or magneto electroencephalography (MEG).

A method and system for generating a probability value for an event. The system includes a source for generating a plurality of digital input signals, a processor connected to the source to receive the plurality of digital input signals from the source, and a display connected to the processor for displaying a final output. Preferably, the method further includes validating the probability value.

An apparatus and method for monitoring a driver using a brain wave signal of the driver are disclosed. The monitoring method includes collecting a brain wave signal for a driver in a vehicle for a predetermined time, determining the driver's state by analyzing the brain wave signal collected for the predetermined time, and controlling an operation of the vehicle on the basis of the determined driver's state.